Existing technology for electronically tracking herds of livestock typically involve storing data on radio-frequency identification tags, and using scanners to interrogate and obtain data from those tags. Present scanning techniques, however, have disadvantages that limit its utility in collecting and processing livestock-related information. For example, scanning distance using low-frequency interrogation systems is on the order of centimeters, meaning that the interrogation devices must be in close proximity to the livestock and RFID tags from which data is to be collected. Further, low-frequency scanners can only scan one RFID tag at a time, do not allow for simultaneous interrogation of multiple tags in a single instance or sweep.
This has the practical limitation of limiting the data pipeline of collections over a large geographical area. Therefore, obtaining such information and moving it into cloud-based storage paradigms is not common practice in the livestock management industry, because the issues described above severely impact the ability to perform advanced data analytics on livestock over wider geographical areas.
Another problem faced by the livestock industry is a limited ability to process data collected by interrogating radio-frequency identification tags for large numbers of livestock over a wide geographical area, and analyzing such information by region, by farm, by feedlot, by pasture, by pen, or by any other such metric. In other words, the combined nature of collecting data and analyzing livestock across a wide area means that an application utilizing artificial intelligence techniques in a data mining process that folds RFID tag data with additional data sources representing weather, markets, and other relevant information, is limited by the ability to interrogate tags and obtain data needed for such analytics.
Solutions to the problems above are key due to increased attention on food security and traceability. Therefore, being able to track and process livestock in a combined approach that is able to quickly obtain and store data across wide distances and for multiple regions is helpful for many reasons, such as monitoring animal health, understanding and promoting improvements in livestock growth and milk production, modeling feed intake rate and inventory needs over the course of a growing season or feeding period, and enhancing food system sustainability.
There is therefore a need in the existing art for improvements in collecting livestock data over a wide geographical area and in the ability to analyze livestock data attributes using such data, in an approach that applies artificial intelligence techniques to predictive data analytics and which combines RFID tag data with other data to better understand and manage the many issues attendant to maintaining a livestock population.